Wang-chiew Tan

• PhD
• Professor (Full) at University of California, Santa Cruz

On October 19 and 20, 2023, the authors of this report convened in Cambridge, MA, to discuss the state of the database research field, its recent accomplishments and ongoing challenges, and future directions for research and community engagement. This gathering continues a long standing tradition in the database community, dating back to the late 1...

Lifelogs are descriptions of experiences that a person had during their life. Lifelogs are created by fusing data from the multitude of digital services, such as online photos, maps, shopping and content streaming services. Question answering over lifelogs can offer personal assistants a critical resource when they try to provide advice in context....

We present a reality check on large language models and inspect the promise of retrieval augmented language models in comparison. Such language models are semi-parametric, where models integrate model parameters and knowledge from external data sources to make their predictions, as opposed to the parametric nature of vanilla large language models....

We present \(\textsf{Ditto}\), a novel entity matching system based on pre-trained Transformer language models. We fine-tune and cast EM as a sequence-pair classification problem to leverage such models with a simple architecture. Our experiments show that a straightforward application of language models such as BERT, DistilBERT, or RoBERTa pre-tra...

In recent years, we have witnessed the development of novel data augmentation (DA) techniques for creating additional training data needed by machine learning based solutions. In this tutorial, we will provide a comprehensive overview of techniques developed by the data management community for data preparation and data integration. In addition to...

Inferring meta information about tables, such as column headers or relationships between columns, is an active research topic in data management as we find many tables are missing some of this information. In this paper, we study the problem of annotating table columns (i.e., predicting column types and the relationships between columns) using only...

Recent approaches for unsupervised opinion summarization have predominantly used the review reconstruction training paradigm. An encoder-decoder model is trained to reconstruct single reviews and learns a latent review encoding space. At summarization time, the unweighted average of latent review vectors is decoded into a summary. In this paper, we...

Entity matching refers to the task of determining whether two different representations refer to the same real-world entity. It continues to be a prevalent problem for many organizations where data resides in different sources and duplicates the need to be identified and managed. The term “entity matching” also loosely refers to the broader problem...

Online users are constantly seeking experiences, such as a hotel with clean rooms and a lively bar, or a restaurant for a romantic rendezvous. However, e-commerce search engines only support queries involving objective attributes such as location, price, and cuisine, and any experiential data is relegated to text reviews. In order to support experi...

This document collects the experiences and advice from the organizers of the SIGMOD/PODS 2020, which shifted on short notice to an online-only conference. It is mainly intended for others who are organizing online conferences, but some of it may be of use in the future to people organizing ?live? conferences with an online component.

We present Ditto, a novel entity matching system based on pre-trained Transformer-based language models. We fine-tune and cast EM as a sequence-pair classification problem to leverage such models with a simple architecture. Our experiments show that a straight-forward application of language models such as BERT, DistilBERT, or RoBERTa pre-trained o...

Detecting the semantic types of data columns in relational tables is important for various data preparation and information retrieval tasks such as data cleaning, schema matching, data discovery, and semantic search. However, existing detection approaches either perform poorly with dirty data, support only a limited number of semantic types, fail t...

Semantic tagging, which has extensive applications in text mining, predicts whether a given piece of text conveys the meaning of a given semantic tag. The problem of semantic tagging is largely solved with supervised learning and today, deep learning models are widely perceived to be better for semantic tagging. However, there is no comprehensive s...

Semantic tagging, which has extensive applications in text mining, predicts whether a given piece of text conveys the meaning of a given semantic tag. The problem of semantic tagging is largely solved with supervised learning and today, deep learning models are widely perceived to be better for semantic tagging. However, there is no comprehensive s...

We present ExplainIt, a review summarization system centered around opinion explainability: the simple notion of high-level opinions (e.g. "noisy room") being explainable by lower-level ones (e.g., "loud fridge"). ExplainIt utilizes a combination of supervised and unsupervised components to mine the opinion phrases from reviews and organize them in...

Creating and collecting labeled data is one of the major bottlenecks in machine learning pipelines and the emergence of automated feature generation techniques such as deep learning, which typically requires a lot of training data, has further exacerbated the problem. While weak-supervision techniques have circumvented this bottleneck, existing fra...

We present OpinionDigest, an abstractive opinion summarization framework, which does not rely on gold-standard summaries for training. The framework uses an Aspect-based Sentiment Analysis model to extract opinion phrases from reviews, and trains a Transformer model to reconstruct the original reviews from these extractions. At summarization time,...

Subjectivity is the expression of internal opinions or beliefs which cannot be objectively observed or verified, and has been shown to be important for sentiment analysis and word-sense disambiguation. Furthermore, subjectivity is an important aspect of user-generated data. In spite of this, subjectivity has not been investigated in contexts where...

Review comprehension has played an increasingly important role in improving the quality of online services and products and commonsense knowledge can further enhance review comprehension. However, existing general-purpose commonsense knowledge bases lack sufficient coverage and precision to meaningfully improve the comprehension of domain-specific...

We present Emu, a system that semantically enhances multilingual sentence embeddings. Our framework fine-tunes pre-trained multilingual sentence embeddings using two main components: a semantic classifier and a language discriminator. The semantic classifier improves the semantic similarity of related sentences, whereas the language discriminator e...

Reviews are integral to e-commerce services and products. They contain a wealth of information about the opinions and experiences of users, which can help better understand consumer decisions and improve user experience with products and services. Today, data scientists analyze reviews by developing rules and models to extract, aggregate, and under...

We present Ditto, a novel entity matching system based on pre-trained Transformer-based language models. We fine-tune and cast EM as a sequence-pair classification problem to leverage such models with a simple architecture. Our experiments show that a straightforward application of language models such as BERT, DistilBERT, or ALBERT pre-trained on...

Existing e-commerce search engines typically support search only over objective attributes, such as price and locations, leaving the more desirable subjective attributes, such as romantic vibe and worklife balance unsearchable. We found that this is also the case for Recruit Group, which operates a wide range of online booking and search services,...

Online services are interested in solutions to opinion mining, which is the problem of extracting aspects, opinions, and sentiments from text. One method to mine opinions is to leverage the recent success of pre-trained language models which can be fine-tuned to obtain high-quality extractions from reviews. However, fine-tuning language models stil...

Reviews are integral to e-commerce services and products. They contain a wealth of information about the opinions and experiences of users, which can help better understand consumer decisions and improve user experience with products and services. Today, data scientists analyze reviews by developing rules and models to extract, aggregate, and under...

Detecting the semantic types of data columns in relational tables is important for various data preparation and information retrieval tasks such as data cleaning, schema matching, data discovery, and semantic search. However, existing detection approaches either perform poorly with dirty data, support only a limited number of semantic types, fail t...

Paraphrases are important linguistic resources for a wide variety of NLP applications. Many techniques for automatic paraphrase mining from general corpora have been proposed. While these techniques are successful at discovering generic paraphrases, they often fail to identify domain-specific paraphrases (e.g., {staff, concierge} in the hospitality...

We present Emu, a system that semantically enhances multilingual sentence embeddings. Our framework fine-tunes pre-trained multilingual sentence embeddings using two main components: a semantic classifier and a language discriminator. The semantic classifier improves the semantic similarity of related sentences, whereas the language discriminator e...

They say a lot of good things in life are not free. Success is one of them. Successful research requires an immense amount of hard work and dedication over a long period of time. For better or worse, hard work alone does not guarantee success. In my experience, success is a marathon of hard work and some luck along the way. What is often forgotten...

Understanding what makes people happy is a central topic in psychology. Prior work has mostly focused on developing self-reporting assessment tools for individuals and relies on experts to analyze the periodic reported assessments. One of the goals of the analysis is to understand what actions are necessary to encourage modifications in the behavio...

We introduce Jo, a mobile application that attempts to improve user's well-being. Jo is a journaling application--users log their important moments via short texts and optionally an attached photo. Unlike a static journal, Jo analyzes these moments and helps users take action towards increased well-being. For example, Jo annotates each moment with...

Online users are constantly seeking experiences, such as a hotel with clean rooms and a lively bar, or a restaurant for a romantic rendezvous. However, e-commerce search engines only support queries involving objective attributes such as location, price, and cuisine, and any experiential data is relegated to text reviews. In order to support experi...

We describe Voyageur, which is an application of experiential search to the domain of travel. Unlike traditional search engines for online services, experiential search focuses on the experiential aspects of the service under consideration. In particular, Voyageur needs to handle queries for subjective aspects of the service (e.g., quiet hotel, fri...

We describe Voyageur, which is an application of experiential search to the domain of travel. Unlike traditional search engines for online services, experiential search focuses on the experiential aspects of the service under consideration. In particular, Voyageur needs to handle queries for subjective aspects of the service (e.g., quiet hotel, fri...

Open Information Extraction (OpenIE) extracts meaningful structured tuples from free-form text. Most previous work on OpenIE considers extracting data from one sentence at a time. We describe NeurON, a system for extracting tuples from question-answer pairs. Since real questions and answers often contain precisely the information that users care ab...

Research into data provenance has been active for almost twenty years. What has it delivered and where will it go next? What practical impact has it had and what might it have? We provide speculative answers to these questions which may be somewhat biased by our initial motivation for studying the topic: the need for provenance information in curat...

Online users are constantly seeking experiences, such as a hotel with clean rooms and a lively bar, or a restaurant for a romantic rendezvous. However, e-commerce search engines only support queries involving objective attributes such as location, price and cuisine, and any experiential data is relegated to text reviews. In order to support experie...

We develop a unifying approach to declarative entity linking by introducing the notion of an entity-linking framework and an accompanying notion of the certain links in such a framework. In an entity-linking framework, logic-based constraints are used to express properties of the desired link relations in terms of source relations and, possibly, in...

Koko is a declarative information extraction system that incorporates advances in natural language processing techniques in its extraction language. Koko's extraction language supports simultaneous specification of conditions over the surface syntax and on the structure of the dependency parse tree of sentences, thereby allowing for more refined ex...

Fraud detection rules, written by domain experts, are often employed by financial companies to enhance their machine learning-based mechanisms for accurate detection of fraudulent transactions. Accurate rule writing is a challenging task where domain experts spend significant effort and time. A key observation is that much of this difficulty origin...

Schema mappings are syntactic specifications of the relationship between two database schemas, typically called the source schema and the target schema. They have been used extensively in formalizing and analyzing data inter-operability tasks, especially data exchange and data integration. There is a growing body of research on deriving schema mapp...

We present the KOKO system that takes declarative information extraction to a new level by incorporating advances in natural language processing techniques in its extraction language. KOKO is novel in that its extraction language simultaneously supports conditions on the surface of the text and on the structure of the dependency parse tree of sente...

We present the Koko system that takes declarative information extraction to a new level by incorporating advances in natural language processing techniques in its extraction language. Koko is novel in that its extraction language simultaneously supports conditions on the surface of the text and on the structure of the dependency parse tree of sente...

The science of happiness is an area of positive psychology concerned with understanding what behaviors make people happy in a sustainable fashion. Recently, there has been interest in developing technologies that help incorporate the findings of the science of happiness into users' daily lives by steering them towards behaviors that increase happin...

The field of data integration has expanded significantly over the years, from providing a uniform query and update interface to structured databases within an enterprise to the ability to search, ex- change, and even update, structured or unstructured data that are within or external to the enterprise. This paper describes the evolution in the land...

In recent years, data examples have been at the core of several different approaches to schema-mapping design. In particular, Gottlob and Senellart introduced a framework for schema-mapping discovery from a single data example, in which the derivation of a schema mapping is cast as an optimization problem. Our goal is to refine and study this frame...

Historical data (also called long data) holds the key to understanding when facts are true. It is through long data that one can understand the trends that have developed in the past, form the audit trails needed for justification, and make predictions about the future. For searching, there is also increasing interest to develop search capabilities...

Data exchange is the problem of transforming data that is structured under the source schema into data structured under another schema, called the target schema, so that both source and target data satisfy the relationship between the schemas. Many applications such as planning, scheduling, medical and fraud detection systems, require data exchange...

Credit card frauds are unauthorized transactions that are made or attempted by a person or an organization that is not authorized by the card holders. In addition to machine learning-based techniques, credit card companies often employ domain experts to manually specify rules that exploit domain knowledge for improving the detection process. Over t...

We introduce and develop a declarative framework for entity linking and, in particular, for entity resolution. As in some earlier approaches, our framework is based on a systematic use of constraints. However, the constraints we adopt are link-to-source constraints, unlike in earlier approaches where source-to-link constraints were used to dictate...

With the abundant availability of information one can mine from the Web today, there is increasing interest to develop a complete understanding of the history of an entity (i.e., a person, a company, a music genre, a country, etc.) (see, for example, [7, 9, 10, 11]) and to depict trends over time [5, 12, 13]. This, however, remains a largely diffic...

As key decisions are often made based on information contained in a database, it is important for the database to be as complete and correct as possible. For this reason, many data cleaning tools have been developed to automatically resolve inconsistencies in databases. However, data cleaning tools provide only best-effort results and usually canno...

To harness the rich amount of information available on the Web today, many organizations start to aggregate public (and private) data to derive new knowledge bases. A fundamental challenge in constructing an accurate integrated knowledge repository from different data sources is to understand how facts across different sources are related to one an...

As key decisions are often made based on information contained in a database, it is important for the database to be as complete and correct as possible. For this reason, many data cleaning tools have been developed to automatically resolve inconsistencies in databases. However, data cleaning tools provide only best-effort results and usually canno...

In recent years, data examples have been at the core of several different approaches to schema-mapping design. In particular, Gottlob and Senellart introduced a framework for schema-mapping discovery from a single data example, in which the derivation of a schema mapping is cast as an optimization problem. Our goal is to refine and study this frame...

We propose a novel foundational framework for why-not explanations, that is, explanations for why a tuple is missing from a query result. Our why-not explanations leverage concepts from an ontology to provide high-level and meaningful reasons for why a tuple is missing from the result of a query. A key algorithmic problem in our framework is that o...

A complete description of an entity is rarely contained in a single data source, but rather, it is often distributed across different data sources. Applications based on personal electronic health records, sentiment analysis, and financial records all illustrate that significant value can be derived from integrated, consistent, and queryable profil...

Companies are increasingly moving their data processing to the cloud, for reasons of cost, scalability, and convenience, among others. However, hosting multiple applications and storage systems on the same cloud introduces resource sharing and heterogeneous data processing challenges due to the variety of resource usage patterns employed, the varie...

The Web is teeming with rich structured information in the form of HTML tables, which provides us with the opportunity to build a knowledge repository by integrating these tables. An essential problem of web data integration is to discover semantic correspondences between web table columns, and schema matching is a popular means to determine the se...

An inconsistent database is a database that violates one or more integrity constraints. A typical approach for answering a query over an inconsistent database is to first clean the inconsistent database by transforming it to a consistent one and then apply the query to the consistent database. An alternative and more principled approach, known as c...

A fundamental task in data integration and data exchange is the design of schema mappings, that is, high-level declarative specifications of the relationship between two database schemas. Several research prototypes and commercial systems have been developed to facilitate schema-mapping design; a common characteristic of these systems is that they...

The 16th International Conference on Database Theory (ICDT 2013) was held in Genoa, Italy, March 18--22, 2013. Originally biennial, the ICDT conference has been held annually and jointly with EDBT ("Extending Database Technology") since 2009.

One of the fundamental tasks in information integration is to specify the relationships, called schema mappings, between database schemas. Schema mappings specify how data structured under a source schema is to be transformed into data structured under a target schema. The design of schema mappings is usually a non-trivial and time-intensive proces...

Decision-makers increasingly need to bring together multiple models across a broad range of disciplines to guide investment and policy decisions around highly complex issues such as population health and safety. We discuss the use of the Smarter Planet Platform for Analysis Simulation of Health (Splash) for cross-disciplinary modeling, simulation,...

Crowd-based data sourcing is a new and powerful data procurement paradigm that engages Web users to collectively contribute information. In this work, we target the problem of gathering data from the crowd in an economical and principled fashion. We present Ask It!, a system that allows interactive data sourcing applications to effectively determin...

As asserted by the Institute of Medicine, sound health policy and investment decisions require use of "what if" simulation models to analyze the potential impacts of alternative decisions on health outcomes. The challenge is that high-level health decisions require understanding complex interactions of diverse systems across many disciplines both i...

As asserted by the Institute of Medicine, sound health policy and investment decisions require use of "what if" simulation models to analyze the potential impacts of alternative decisions on health outcomes. The challenge is that high-level health decisions require understanding complex interactions of diverse systems across many disciplines both i...

Schema mappings are high-level specifications that describe the relationship between two database schemas; they are considered to be the essential building blocks in data exchange and data integration, and have been the object of extensive research investigations. Since in real-life applications schema mappings can be quite complex, it is important...

Rising costs, decreasing quality of care, diminishing productivity, and increasing complexity have all contributed to the present state of the healthcare industry. The interactions between payers (e.g., insurance companies and health plans) and providers (e.g., hospitals and laboratories) are growing and are becoming more complicated. The constant...

Current database technology has raised the art of scalable descriptive analytics to a very high level. Unfortunately, what enterprises really need is prescriptive analytics to identify optimal business, policy, investment, and engineering decisions in the face of uncertainty. Such analytics, in turn, rest on deep predictive analytics that go beyond...

One of the first steps in the process of integrating information from multiple sources into a desired target format is to specify the re-lationships, called schema mappings, between the source schemas and the target schema. In this demonstration, we showcase a new methodology for designing schema mappings. Our system Eirene interactively solicits d...

A schema mapping is a specification of the relationship between a source schema and a target schema. Schema mappings are fundamental building blocks in data integration and data exchange and, as such, obtaining the right schema mapping constitutes a major step towards the integration or exchange of data. Up to now, schema mappings have typically be...

An inverse of a schema mapping M is intended to "undo" what M does, thus providing a way to perform "reverse" data exchange. In recent years, three different formalizations of this concept have been introduced and stud- ied, namely, the notions of an inverse of a schema mapping, a quasi-inverse of a schema mapping, and a maximum recovery of a schem...